# -×- coding: utf-8 -*-
# https://github.com/EternityZY/FCN-TensorFlow/blob/master/FCN.py


from __future__ import print_function
import tensorflow as tf
import numpy as np

import TensorflowUtils as utils
import read_MITSceneParsingData as scene_parsing
import datetime
import BatchDatsetReader as dataset
from six.moves import xrange

FLAGS = tf.flags.FLAGS
tf.flags.DEFINE_integer("batch_size", "2", "batch size for training")
tf.flags.DEFINE_string("logs_dir", "logs/", "path to logs directory")
tf.flags.DEFINE_string("data_dir", "Data_zoo/MIT_SceneParsing/", "path to dataset")
tf.flags.DEFINE_float("learning_rate", "1e-4", "Learning rate for Adam Optimizer")
tf.flags.DEFINE_string("model_dir", "Model_zoo/", "Path to vgg model mat")
tf.flags.DEFINE_bool('debug', "False", "Debug mode: True/ False")
tf.flags.DEFINE_string('mode', "train", "Mode train/ test/ visualize")

MODEL_URL = 'http://www.vlfeat.org/matconvnet/models/beta16/imagenet-vgg-verydeep-19.mat'

MAX_ITERATION = int(1e5 + 1)
NUM_OF_CLASSESS = 151
IMAGE_SIZE = 224


## vgg 网络部分， weights 是vgg网络各层的权重集合， image是被预测的图像的向量
## JM: 所以VGG网络的卷积层是不改变图片大小的（因为使用了Same Padding），只有池化层会改变
def vgg_net(weights, image):

    ## fcn的前五层网络就是vgg网络，一共16个卷积层，故名为vgg16
    layers = (  # JM: 假设原图为224 * 224 * 3
        'conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1', # JM: 两个3 * 3 * 3 * 64的卷积后，经过一层池化(2*2步幅2)，大小变为 B *112 * 112 * 64 (same padding)

        'conv2_1', 'relu2_1', 'conv2_2', 'relu2_2', 'pool2', # JM: 两个3 * 3 * 64 * 128的卷积后，经过一层池化，大小变为 B * 56 * 56 * 128(same padding)

        'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', 'conv3_3', # JM: 三个3 * 3 * 128 * 256的卷积后，经过一层池化，大小变为 B * 28 * 28 * 256
        'relu3_3', 'conv3_4', 'relu3_4', 'pool3',

        'conv4_1', 'relu4_1', 'conv4_2', 'relu4_2', 'conv4_3', # JM: 四个3 * 3 * 256 * 512的卷积后，经过一层池化，大小变为 B * 14 * 14 * 512
        'relu4_3', 'conv4_4', 'relu4_4', 'pool4',

        'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2', 'conv5_3', # JM：四个3 * 3 * 3 * 512的的卷积后，经过一层池化，大小变为 B * 7 * 7 * 512
        'relu5_3', 'conv5_4', 'relu5_4'
    )

    net = {}
    current = image
    for i, name in enumerate(layers):
        kind = name[:4]
        if kind == 'conv':
            kernels, bias = weights[i][0][0][0][0]
            # matconvnet: weights are [width, height, in_channels, out_channels]
            # tensorflow: weights are [height, width, in_channels, out_channels]
            # 由于 imagenet-vgg-verydeep-19.mat 中的参数矩阵和我们定义的长宽位置颠倒了
            # 原来索引号（reshape(2,2,3)）是012，现在是102
            kernels = utils.get_variable(np.transpose(kernels, (1, 0, 2, 3)), name=name + "_w") #(1, 0, 2, 3)是索引号
            bias = utils.get_variable(bias.reshape(-1), name=name + "_b") #reshape(-1)把bias参数数组合并成一行
            current = utils.conv2d_basic(current, kernels, bias)
        elif kind == 'relu':
            current = tf.nn.relu(current, name=name)
            if FLAGS.debug:
                utils.add_activation_summary(current)
        elif kind == 'pool':
            ## vgg 的前5层的stride都是2，也就是前5层的size依次减小1倍
            ## 这里处理了前4层的stride，用的是平均池化
            ## 第5层的pool在下文的外部处理了，用的是最大池化
            ## pool1 size缩小2倍
            ## pool2 size缩小4倍
            ## pool3 size缩小8倍
            ## pool4 size缩小16倍
            current = utils.avg_pool_2x2(current)  ## 平均池化
        net[name] = current

    return net  ## vgg每层的结果都保存再net中了


## 预测流程，image是输入图像的向量，keep_prob是dropout rate
def inference(image, keep_prob):
    """
    Semantic segmentation network definition
    ## 语义分割网络
    :param image: input image. Should have values in range 0-255
    :param keep_prob:#keep_prob: 名字代表的意思, keep_prob 参数可以为 tensor，意味着，训练时候 feed 为0.5，
    :return:
    """

    ## 获取训练好的vgg部分的model
    print("setting up vgg initialized conv layers ...") #设置vgg初始化的conv层
    model_data = utils.get_model_data(FLAGS.model_dir, MODEL_URL)

    mean = model_data['normalization'][0][0][0] #这里个人认为上述加载后的模型保存在一个类似于字典的结构里。
    mean_pixel = np.mean(mean, axis=(0, 1)) #获取图片像素的均值

    weights = np.squeeze(model_data['layers'])  # JM: 拿到vgg预训练的权值

    ## 将图像的向量值都减去平均像素值，进行 normalization
    processed_image = utils.process_image(image, mean_pixel)

    with tf.variable_scope("inference"):
        ## 计算前5层vgg网络的输出结果
        image_net = vgg_net(weights, processed_image)  # JM: 前向走标准的vgg网络拿到图像特征图
        conv_final_layer = image_net["conv5_3"]

        ## pool1 size缩小2倍
        ## pool2 size缩小4倍
        ## pool3 size缩小8倍
        ## pool4 size缩小16倍
        ## pool5 size缩小32倍
        pool5 = utils.max_pool_2x2(conv_final_layer)

        ## 初始化第6层的w、b
        ## 7*7 卷积核的视野很大
        W6 = utils.weight_variable([7, 7, 512, 4096], name="W6")  # JM: 还奇怪为啥卷积层也有w，原来就是卷积核尺寸(高7*宽7*512通道*4096个filter) JM: 512可以理解为输入通道，4096为输出通道www
        b6 = utils.bias_variable([4096], name="b6")  # JM: 这个就是相应的b，第6层卷积层的参数两个在这里
        conv6 = utils.conv2d_basic(pool5, W6, b6)
        relu6 = tf.nn.relu(conv6, name="relu6")
        if FLAGS.debug:
            utils.add_activation_summary(relu6)
        relu_dropout6 = tf.nn.dropout(relu6, keep_prob=keep_prob)
        ## 在第6层没有进行池化，所以经过第6层后 size缩小仍为32倍

        ## 初始化第7层的w、b
        W7 = utils.weight_variable([1, 1, 4096, 4096], name="W7")
        b7 = utils.bias_variable([4096], name="b7")
        conv7 = utils.conv2d_basic(relu_dropout6, W7, b7)
        relu7 = tf.nn.relu(conv7, name="relu7")
        if FLAGS.debug:
            utils.add_activation_summary(relu7)
        relu_dropout7 = tf.nn.dropout(relu7, keep_prob=keep_prob)
        ## 在第7层没有进行池化，所以经过第7层后 size缩小仍为32倍

        ## 初始化第8层的w、b
        ## 输出维度为NUM_OF_CLASSESS
        W8 = utils.weight_variable([1, 1, 4096, NUM_OF_CLASSESS], name="W8")  # JM: 这里应该就是理解为像素分类了将4096张图映射至151个类别上
        b8 = utils.bias_variable([NUM_OF_CLASSESS], name="b8")
        conv8 = utils.conv2d_basic(relu_dropout7, W8, b8)
        # annotation_pred1 = tf.argmax(conv8, dimension=3, name="prediction1")
        """
        Encoder 结束
        Decoder 开始
        """
        # now to upscale to actual image size
        ## 开始将size提升为图像原始尺寸  # JM: 上采样是一步到位的？这个例子中似乎不是
        deconv_shape1 = image_net["pool4"].get_shape()  # (?, 14, 14, 512)  # 将pool4 即1/16结果尺寸拿出来 做融合 [b,h,w,c] (batch_size, height, weight, 通道数)，漏了一个batch_size维度 # JM: 这个形状应该是input经过pool4以后的size，所以应该是14 * 14 * 512，这里的shape到底是啥。。。
        W_t1 = utils.weight_variable([4, 4, deconv_shape1[3].value, NUM_OF_CLASSESS], name="W_t1")  # (4, 4, 512, 151)  # JM: 卷积核大小为[4, 4, 512, 151] # 4*4的卷积核，输入通道数pool4的通道数，输出通道数NUM_OF_CLASSESS
        b_t1 = utils.bias_variable([deconv_shape1[3].value], name="b_t1")
        ## 对第8层的结果进行反卷积(上采样),通道数也由NUM_OF_CLASSESS变为第4层的通道数
        conv_t1 = utils.conv2d_transpose_strided(conv8, W_t1, b_t1, output_shape=tf.shape(image_net["pool4"]))
        ## 对应论文原文中的"2× upsampled prediction + pool4 prediction"
        fuse_1 = tf.add(conv_t1, image_net["pool4"], name="fuse_1")

        deconv_shape2 = image_net["pool3"].get_shape()  # (?, 28, 28, 256)  # JM: 这个形状应该是[1,2,2,1]
        ## 对上一层上采样的结果进行反卷积(上采样),通道数也由上一层的通道数变为第3层的通道数
        W_t2 = utils.weight_variable([4, 4, deconv_shape2[3].value, deconv_shape1[3].value], name="W_t2")  # (4, 4, 256, 512)  # JM: 卷积核大小为[4, 4, 1, 1]
        b_t2 = utils.bias_variable([deconv_shape2[3].value], name="b_t2")
        conv_t2 = utils.conv2d_transpose_strided(fuse_1, W_t2, b_t2, output_shape=tf.shape(image_net["pool3"]))  # (?, 28, 28, 256)
        ## 对应论文原文中的"2× upsampled prediction + pool3 prediction"
        fuse_2 = tf.add(conv_t2, image_net["pool3"], name="fuse_2")

        ## 原始图像的height、width和通道数
        shape = tf.shape(image)  # (?, 244, 244, 3)
        ## 既形成一个列表，形式为[height, width, in_channels, out_channels]
        deconv_shape3 = tf.stack([shape[0], shape[1], shape[2], NUM_OF_CLASSESS])
        W_t3 = utils.weight_variable([16, 16, NUM_OF_CLASSESS, deconv_shape2[3].value], name="W_t3")  # (16, 16, 151, 256)  # JM: 卷积核大小为[16, 16, 151, 4]
        b_t3 = utils.bias_variable([NUM_OF_CLASSESS], name="b_t3")
        ## 再进行一次反卷积，将上一层的结果转化为和原始图像相同size、通道数为分类数的形式数据
        conv_t3 = utils.conv2d_transpose_strided(fuse_2, W_t3, b_t3, output_shape=deconv_shape3, stride=8)

        ## 目前conv_t3的形式为size为和原始图像相同的size，通道数与分类数相同
        ## 这句我的理解是对于每个像素位置，根据第3维度（通道数）通过argmax能计算出这个像素点属于哪个分类
        ## 也就是对于每个像素而言，NUM_OF_CLASSESS个通道中哪个数值最大，这个像素就属于哪个分类
        annotation_pred = tf.argmax(conv_t3, dimension=3, name="prediction")

    return tf.expand_dims(annotation_pred, dim=3), conv_t3


## 训练: 定义训练损失优化器及训练的梯度下降方法以更新参数
def train(loss_val, var_list): #测试损失
    optimizer = tf.train.AdamOptimizer(FLAGS.learning_rate)
    ## 下面是参照tf api
    ## Compute gradients of loss_val for the variables in var_list.
    ## This is the first part of minimize().
    ## loss: A Tensor containing the value to minimize.
    ## var_list: Optional list of tf.Variable to update to minimize loss.
    ##   Defaults to the list of variables collected in the graph under the key GraphKey.TRAINABLE_VARIABLES.
    grads = optimizer.compute_gradients(loss_val, var_list=var_list)
    if FLAGS.debug:
        # print(len(var_list))
        for grad, var in grads:
            utils.add_gradient_summary(grad, var)
    ## 下面是参照tf api
    ## Apply gradients to variables.
    ## This is the second part of minimize(). It returns an Operation that applies gradients.
    return optimizer.apply_gradients(grads)

#主函数
def main(argv=None):
    ## dropout 的保留率
    keep_probability = tf.placeholder(tf.float32, name="keep_probabilty")
    ## 原始图像的向量 #定义原图和标签的占位符用来动态存储传入的图片
    image = tf.placeholder(tf.float32, shape=[None, IMAGE_SIZE, IMAGE_SIZE, 3], name="input_image") #原始图像的形式，None为自动查看相应的样本数
    ## 原始图像对应的标注图像的向量
    annotation = tf.placeholder(tf.int32, shape=[None, IMAGE_SIZE, IMAGE_SIZE, 1], name="annotation") #原始图片对应的标签形式

    ## 输入原始图像向量、保留率，得到预测的标注图像和随后一层的网络输出
    pred_annotation, logits = inference(image, keep_probability)
    ## 为了方便查看图像预处理的效果，可以利用 TensorFlow 提供的 tensorboard 工具进行可视化，直接用 tf.summary.image 将图像写入 summary
    tf.summary.image("input_image", image, max_outputs=2)
    tf.summary.image("ground_truth", tf.cast(annotation, tf.uint8), max_outputs=2)
    tf.summary.image("pred_annotation", tf.cast(pred_annotation, tf.uint8), max_outputs=2)
    ## 计算预测标注图像和真实标注图像的交叉熵
    loss = tf.reduce_mean((tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits,
                                                                          labels=tf.squeeze(annotation, squeeze_dims=[3]),
                                                                          name="entropy")))
    tf.summary.scalar("entropy", loss)

    ## 返回需要训练的变量列表
    trainable_var = tf.trainable_variables()
    if FLAGS.debug:
        for var in trainable_var:
            utils.add_to_regularization_and_summary(var)
    ## 定义损失
    train_op = train(loss, trainable_var)

    print("Setting up summary op...")
    ## 定义合并变量操作，一次性生成所有摘要数据
    summary_op = tf.summary.merge_all()

    print("Setting up image reader...")
    ## 读取训练数据集、验证数据集
    train_records, valid_records = scene_parsing.read_dataset(FLAGS.data_dir)
    print(len(train_records))
    print(len(valid_records))

    print("Setting up dataset reader")
    ## 将训练数据集、验证数据集的格式转换为网络需要的格式
    image_options = {'resize': True, 'resize_size': IMAGE_SIZE}
    if FLAGS.mode == 'train':
        train_dataset_reader = dataset.BatchDatset(train_records, image_options)
    validation_dataset_reader = dataset.BatchDatset(valid_records, image_options)

    sess = tf.Session()

    print("Setting up Saver...")
    saver = tf.train.Saver()
    summary_writer = tf.summary.FileWriter(FLAGS.logs_dir, sess.graph)

    sess.run(tf.global_variables_initializer())
    ## 加载之前的checkpoint
    ckpt = tf.train.get_checkpoint_state(FLAGS.logs_dir)
    if ckpt and ckpt.model_checkpoint_path:
        saver.restore(sess, ckpt.model_checkpoint_path)
        print("Model restored...")

    if FLAGS.mode == "train":
        for itr in xrange(MAX_ITERATION):
            ## 读取训练集的一个batch
            train_images, train_annotations = train_dataset_reader.next_batch(FLAGS.batch_size)
            feed_dict = {image: train_images, annotation: train_annotations, keep_probability: 0.85}

            ## 执行计算损失操作，网络跑起来了
            sess.run(train_op, feed_dict=feed_dict)

            if itr % 10 == 0:
                train_loss, summary_str = sess.run([loss, summary_op], feed_dict=feed_dict)
                print("Step: %d, Train_loss:%g" % (itr, train_loss))
                summary_writer.add_summary(summary_str, itr)

            if itr % 500 == 0:
                valid_images, valid_annotations = validation_dataset_reader.next_batch(FLAGS.batch_size)
                valid_loss = sess.run(loss, feed_dict={image: valid_images, annotation: valid_annotations,
                                                       keep_probability: 1.0})
                print("%s ---> Validation_loss: %g" % (datetime.datetime.now(), valid_loss))
                saver.save(sess, FLAGS.logs_dir + "model.ckpt", itr)

    elif FLAGS.mode == "visualize":
        valid_images, valid_annotations = validation_dataset_reader.get_random_batch(FLAGS.batch_size)
        pred = sess.run(pred_annotation, feed_dict={image: valid_images, annotation: valid_annotations,
                                                    keep_probability: 1.0})
        valid_annotations = np.squeeze(valid_annotations, axis=3)
        pred = np.squeeze(pred, axis=3)

        for itr in range(FLAGS.batch_size):
            utils.save_image(valid_images[itr].astype(np.uint8), FLAGS.logs_dir, name="inp_" + str(5+itr))
            utils.save_image(valid_annotations[itr].astype(np.uint8), FLAGS.logs_dir, name="gt_" + str(5+itr))
            utils.save_image(pred[itr].astype(np.uint8), FLAGS.logs_dir, name="pred_" + str(5+itr))
            print("Saved image: %d" % itr)


if __name__ == "__main__":
    tf.app.run()